Some bio-physical parameters（e.g.leaf area index

LAI） are often inverted using remote sensing data for its large cover scope

high temporal and spatial resolution.The common way of mapping LAI is through the inversion of physically based canopy-reflectance（CR） models using the optimization methods.The information of remote sensing data is usually not enough for the LAI inversion;furthermore

the inversion problem is ill-posed because of many unknown parameters and the relatively insufficient information in remote sensing data.It is necessary to make suitable inversion strategy（such as which parameter（s） should be inverted） for high accuracy of parameters estimation.We should learn the factors which affect the inversion result in order to design the suitable inversion strategy.Different from the research of parameter sensitivity for suitable inversion strategy

we made progress in the inversion process.For the information of the inversion process

some key points are needed to investigate

such as the factors affecting the parameters estimation

the mutual effect of different parameters in inversion process and so on.In the paper

we investigated the factors which affect the parameter estimation from the inversion process aiming at directing the parameter inversion.One of the accuracy indices for the inversion result is the root mean square error（RMSE）.For the inversion result

the smaller RMSE is

the higher inversion accuracy is.We investigated the formulae of the RMSE based on the physically based canopy-reflectance model.Through mathematical formulae and physical mechanism

we can know that the factors affecting the RMSE consist of canopy reflectance data quality

the sensitivity of parameters and the correlation of the parameter sensitivity.That is to say

as to the sensitivity of parameters

not only the parameter sensitivity but the correlation of the parameter sensitivity the factors affect the parameters inversion accuracy.In other words

the relative sensitivity of the parameter has effect on the parameter inversion.We should make two kinds of progress for high accuracy parameter inversion.One is about the quality of canopy reflectance data.Remote sensing data are often contaminated with noise from various sources

such as radiation calibration

atmosphere correction

geometric registration and some random noises.The other is the sensitivities of the parameters and the correlation of the parameter sensitivity.We can make the suitable inversion strategy based on both the quality of the canopy reflectance data and the parameters sensitivities.The CR model is the SAIL model and the inversion method is the modified least square method in this paper.We validated the factors which affect the LAI inversion accuracy through LAI inversion based on simulated CR data sets.